1 R Session Information

## R version 4.1.1 (2021-08-10)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] bayestestR_0.13.1  rstanarm_2.21.4    Rcpp_1.0.10        ggprism_1.0.4     
##  [5] interactions_1.1.5 afex_1.3-0         lmerTest_3.1-3     lme4_1.1-33       
##  [9] Matrix_1.3-4       sjPlot_2.8.14      lubridate_1.9.2    forcats_1.0.0     
## [13] stringr_1.5.0      dplyr_1.1.2        purrr_1.0.1        readr_2.1.4       
## [17] tidyr_1.3.0        tibble_3.2.1       ggplot2_3.4.2      tidyverse_2.0.0   
## [21] plyr_1.8.8        
## 
## loaded via a namespace (and not attached):
##   [1] backports_1.4.1      jtools_2.2.1         igraph_1.4.2        
##   [4] splines_4.1.1        crosstalk_1.2.0      TH.data_1.1-2       
##   [7] rstantools_2.3.1     inline_0.3.19        digest_0.6.31       
##  [10] htmltools_0.5.5      fansi_1.0.4          magrittr_2.0.3      
##  [13] tzdb_0.3.0           modelr_0.1.11        RcppParallel_5.1.7  
##  [16] matrixStats_0.63.0   vroom_1.6.3          xts_0.13.1          
##  [19] sandwich_3.0-2       timechange_0.2.0     prettyunits_1.1.1   
##  [22] colorspace_2.1-0     xfun_0.39            callr_3.7.3         
##  [25] crayon_1.5.2         jsonlite_1.8.4       survival_3.5-5      
##  [28] zoo_1.8-12           glue_1.6.2           gtable_0.3.3        
##  [31] emmeans_1.8.5        sjstats_0.18.2       sjmisc_2.8.9        
##  [34] car_3.1-2            pkgbuild_1.4.0       rstan_2.21.8        
##  [37] abind_1.4-5          scales_1.2.1         mvtnorm_1.1-3       
##  [40] DBI_1.1.3            ggeffects_1.2.2      miniUI_0.1.1.1      
##  [43] xtable_1.8-4         performance_0.10.4   bit_4.0.5           
##  [46] stats4_4.1.1         StanHeaders_2.21.0-7 DT_0.28             
##  [49] htmlwidgets_1.6.2    threejs_0.3.3        ellipsis_0.3.2      
##  [52] pkgconfig_2.0.3      loo_2.6.0            sass_0.4.5          
##  [55] utf8_1.2.3           tidyselect_1.2.0     rlang_1.1.1         
##  [58] reshape2_1.4.4       later_1.3.1          munsell_0.5.0       
##  [61] tools_4.1.1          cachem_1.0.8         cli_3.6.1           
##  [64] generics_0.1.3       sjlabelled_1.2.0     broom_1.0.4         
##  [67] evaluate_0.20        fastmap_1.1.1        yaml_2.3.7          
##  [70] processx_3.8.1       knitr_1.42           bit64_4.0.5         
##  [73] pander_0.6.5         nlme_3.1-162         mime_0.12           
##  [76] compiler_4.1.1       bayesplot_1.10.0     shinythemes_1.2.0   
##  [79] rstudioapi_0.14      bslib_0.4.2          stringi_1.7.12      
##  [82] ps_1.7.5             lattice_0.21-8       nloptr_2.0.3        
##  [85] markdown_1.6         shinyjs_2.1.0        vctrs_0.6.2         
##  [88] pillar_1.9.0         lifecycle_1.0.3      jquerylib_0.1.4     
##  [91] estimability_1.4.1   insight_0.19.2       httpuv_1.6.11       
##  [94] R6_2.5.1             promises_1.2.0.1     gridExtra_2.3       
##  [97] codetools_0.2-19     boot_1.3-28.1        colourpicker_1.2.0  
## [100] MASS_7.3-60          gtools_3.9.4         withr_2.5.0         
## [103] shinystan_2.6.0      multcomp_1.4-23      parallel_4.1.1      
## [106] hms_1.1.3            grid_4.1.1           coda_0.19-4         
## [109] minqa_1.2.5          rmarkdown_2.21       carData_3.0-5       
## [112] numDeriv_2016.8-1.1  shiny_1.7.4          base64enc_0.1-3     
## [115] dygraphs_1.1.1.6

2 The effect of socialness on reaction times (log transformed)

2.1 LMM Results

Iterative procedure suggests modelling random intercept and slopes per participant, without slope/intercept correlations. Model with optimal random effects structure: logRT ~ Socialness* Concreteness + (1 + Socialness*Concreteness||participant) + (1|Word)

	logRT
Predictors	Estimates	CI	Statistic	p	df
(Intercept)	0.08	0.05 – 0.10	6.07	<0.001	82.58
Socialness1	-0.02	-0.04 – -0.00	-2.30	0.023	137.64
Concreteness	-0.02	-0.03 – -0.01	-3.87	<0.001	151.75
Socialness1 × Concreteness	-0.00	-0.02 – 0.02	-0.31	0.756	132.67
Random Effects
σ²	0.02
τ₀₀ _Word	0.00
τ₀₀ _{participant.3}	0.01
τ₁₁ _{participant.re1.Socialness1_by_Concreteness}	0.00
τ₁₁ _{participant.re1.Concreteness}	0.00
τ₁₁ _{participant.re1.Socialness1}	0.00
ρ₀₁
ρ₀₁
ICC	0.11
Marginal R² / Conditional R²	0.022 / 0.134

## $Socialness

## 
## $Concreteness

2.1.1 Check Assumptions

2.2 Bayesian ROPE analysis

The Bayesian analyses estimated that 33.25% of the HDI for socialness (98.58% PD), 37.01% for concreteness (100% PD), and 96.14% for the interaction (62.8% PD), fell within the ROPE (-0.017-0.017).

3 The effect of socialness on accuracy

Iterative procedure suggests modelling random intercept and slopes per participant. Model with optimal random effects structure: Accuracy ~ Socialness^Concreteness + (1 + Socialness*Concreteness|participant) + (1|Word)

3.1 LMM

	ACC
Predictors	Odds Ratios	CI	Statistic	p
(Intercept)	12.24	8.82 – 17.00	14.96	<0.001
Socialness1	1.54	1.07 – 2.23	2.30	0.022
Concreteness	1.50	1.25 – 1.82	4.26	<0.001
Socialness1 × Concreteness	0.98	0.68 – 1.39	-0.14	0.892
Random Effects
σ²	3.29
τ₀₀ _Word	0.74
τ₀₀ _participant	1.35
τ₁₁ _{participant.Socialness1}	0.26
τ₁₁ _{participant.Concreteness}	0.09
τ₁₁ _{participant.Socialness1:Concreteness}	0.12
ρ₀₁ _{participant.Socialness1}	0.28
ρ₀₁ _{participant.Concreteness}	-0.33
ρ₀₁ _{participant.Socialness1:Concreteness}	-0.33
ICC	0.41
Marginal R² / Conditional R²	0.035 / 0.430

## $Socialness

## 
## $Concreteness

3.1.1 Check Assumptions

## $Word

## 
## $participant

3.2 ROPE Analysis

The Bayesian analyses estimated that 8.89% of the HDI for socialness (98.67% PD), 0% for concreteness (99.99% PD), and 69.35% for the interaction (52.91% PD), fell within the ROPE (-0.181-0.181).

4 The effect of socialness on raw RTs

4.1 LMM

Iterative procedure suggests modelling random intercept and slopes per participant, without slope/intercept correlations. Model with optimal random effects structure: RT ~ Socialness* Concreteness + (1 + Socialness*Concreteness||participant) + (1|Word)

	RT
Predictors	Estimates	CI	p
(Intercept)	1.30	1.22 – 1.37	<0.001
Socialness1	-0.06	-0.11 – -0.01	0.030
Concreteness	-0.06	-0.09 – -0.03	<0.001
Socialness1 × Concreteness	-0.01	-0.06 – 0.05	0.822
Random Effects
σ²	0.18
τ₀₀ _Word	0.02
τ₀₀ _{participant.3}	0.08
τ₁₁ _{participant.re1.Socialness1_by_Concreteness}	0.00
τ₁₁ _{participant.re1.Concreteness}	0.00
τ₁₁ _{participant.re1.Socialness1}	0.00
ρ₀₁
ρ₀₁
ICC	0.10
N _participant	68
N _Word	134
Observations	7719
Marginal R² / Conditional R²	0.020 / 0.120

## $Socialness

## 
## $Concreteness

4.1.1 Check Assumptions

5 Explore socialness by PoS, controlling for concreteness

	Socialness
Predictors	Estimates	CI	Statistic	p	df
(Intercept)	5.23	5.13 – 5.34	97.16	<0.001	6448.00
Part of Speech [1]	-0.23	-0.30 – -0.15	-5.81	<0.001	6448.00
Concreteness	-0.50	-0.53 – -0.46	-30.50	<0.001	6448.00
R² / R² adjusted	0.127 / 0.127

## quartz_off_screen 
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## quartz_off_screen 
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Study 3 - Data Analysis

Veronica Diveica